Metabarcoding Singapore
ASV from Dada2

1 Aim

  • Assign and analyze eukaryotes for Singapore metabarcoding data (ASV assigned with dada2 as implemented on Mothur).
  • Do some analyzes with the prokaryotes too…

2 Initialize

This file defines all the necessary libraries and variables

  source('Metabarcoding Singapore_init.R', echo=FALSE)

3 Read the data

3.1 File names

full_path_data <- function(file_name) {
    str_c("../qiime/2018-09-06_dada2/", file_name)
}

taxo_file <- full_path_data("taxonomy.tsv")
otu_file <- full_path_data("feature-table_unrarefied.tsv")
sequence_file <- full_path_data("ref_sequences_unrarefied.fasta")

metadata_file <- "../metadata/Singapore_metadata.xlsx"

sequence_file_euk <- full_path_data("ASV_unrarefied_euk.fasta")
dada2_taxo_file_euk <- full_path_data("ASV_unrarefied_euk.dada2.taxo")
dada2_boot_file_euk <- full_path_data("ASV_unrarefied_euk.dada2.boot")
otu_table_final_file <- full_path_data("ASV_final.tsv")

blast_file <- full_path_data("ASV_unrarefied_euk.blast.tsv")

3.2 Read the files

  • The dada2 treatment has already removed the forward and reverse primers, so no need to remove them
  • Work with the unrarefied data
# Read the sample and metadata tables
  sample_table <- read_excel(metadata_file, sheet="samples", range="A1:D89") 
  metadata_table  <- read_excel(metadata_file, sheet="metadata", na=c("ND", ""))
  sample_table <- left_join(sample_table, metadata_table) %>% 
                  mutate(sample_label = str_c(strait_label,location_label,
                                              monsoon,sprintf("%03d",day_number), 
                                              sep="_"))

# Read the taxonomy table
  taxo_table <- read_tsv(taxo_file)

# Clean up the taxonomy
  taxo_table <- taxo_table %>% 
    mutate(taxo_clean = str_replace_all(Taxon, "D_[0-9]+__","")) %>% 
    separate(col=taxo_clean, into=str_c("taxo", c(1:7)), sep=";") %>% 
    rename(otu_name = `Feature ID`)
  
# Read the otu table
  otu_table <- read_tsv(otu_file, skip=1) %>%  # Jump the first line
    rename(otu_name = `#OTU ID`) %>% 
    mutate(otu_id = str_c("otu_", sprintf("%04d",row_number())))

# Read the sequences
  otu_sequences <- readAAStringSet(sequence_file)
  otu_sequences.df <- data.frame (otu_name=names(otu_sequences),sequence=as.character(otu_sequences))

# Remove the primers - Not necessary because the primers have been removed  
  # fwd_length = 20
  # rev_length = 15
  # otu_sequences.df <- otu_sequences.df %>% 
  #   separate (col=names, into=c("otu_id_qiime", "otu_rep_seq"), sep=" ") %>%
  #   mutate (sequence = str_sub(sequence, start=fwd_length+1, end = - rev_length - 1))

  otu_table <- taxo_table %>%  
    left_join(otu_table) %>% 
    left_join(otu_sequences.df) %>% 
    arrange(otu_id)
  
# Write a fasta file for blast with all taxonomy roups
 # otu_sequences <- otu_table %>% transmute(sequence=sequence, seq_name=otu_id)
 # fasta_write(otu_sequences, file_name="../qiime/otu_rep_98_all.fasta", compress = FALSE, taxo_include = FALSE)

3.3 Only keep the eukaryotes in the OTU file

otu_table_euk <- otu_table %>% filter(str_detect(Taxon, "Eukaryota"))

# Write the fasta file file
otu_sequences_euk <- otu_table_euk %>% transmute(sequence = sequence, seq_name = otu_id)
fasta_write(otu_sequences_euk, file_name = sequence_file_euk, compress = FALSE, 
    taxo_include = FALSE)
[1] TRUE

4 Assignment of eukaryotic ASVs based on PR2 database

4.1 Use dada2 to reassign to PR2

dada2_assign(seq_file_name = sequence_file_euk)

4.2 Read the PR2 assignement and merge with initial otu table

otu_euk_pr2 <- read_tsv(dada2_taxo_file_euk)
otu_euk_pr2_boot <- read_tsv(dada2_boot_file_euk) %>% rename_all(funs(str_c(., 
    "_boot"))) %>% rename(seq_name = seq_name_boot)
otu_euk_pr2 <- left_join(otu_euk_pr2, otu_euk_pr2_boot) %>% rename(otu_id = seq_name)
otu_table_final <- left_join(otu_table, otu_euk_pr2) %>% select(otu_id, otu_name, 
    taxo1:taxo7, Taxon, kingdom:species_boot, matches("EC|PR|RM|SBW|STJ"), sequence)

write_tsv(otu_table_final, otu_table_final_file, na = "")

5 Process BLAST file

BLAST is performed on Roscoff ABIMS server

blast_18S_reformat(blast_file)

6 Phyloseq analysis

6.1 Create phyloseq files for euk after filtering the data

Filter the euk data to remove the low bootstraps values (threshold : bootstrap > 90% at the supergroup level) and create a phyloseq file

Note the bootstrap threshold had to be higher for 98% compared to 97% (90% vs 65%). For ASV the same bootstrap has been used

otu_table_euk_final <- otu_table_final %>% filter(supergroup_boot > 90)

otu_mat <- otu_table_euk_final %>% select(otu = otu_id, matches("EC|PR|RM|SBW|STJ"), 
    -species, -species_boot)
tax_mat <- otu_table_euk_final %>% select(otu = otu_id, kingdom:species)
samples_df <- sample_table %>% rename(sample = sample_id)

row.names(otu_mat) <- otu_mat$otu
otu_mat <- otu_mat %>% select(-otu)
row.names(tax_mat) <- tax_mat$otu
tax_mat <- tax_mat %>% select(-otu)
row.names(samples_df) <- samples_df$sample
samples_df <- samples_df %>% select(-sample)
otu_mat <- as.matrix(otu_mat)
tax_mat <- as.matrix(tax_mat)

OTU = otu_table(otu_mat, taxa_are_rows = TRUE)
TAX = tax_table(tax_mat)
samples = sample_data(samples_df)

ps_euk <- phyloseq(OTU, TAX, samples)
ps_euk <- subset_samples(ps_euk, sequence_quality == "good")

6.2 Break up into photosynthetic and non-photosynthetic

  • Opisthokonta (Metazoa, Fungi) are removed
ps_euk <- subset_taxa(ps_euk, !(supergroup %in% c("Opisthokonta")))
cat("\nPhyloseq Eukaryotes \n========== \n")

Phyloseq Eukaryotes 
========== 
ps_euk
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 663 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 663 taxa by 8 taxonomic ranks ]
ps_photo <- subset_taxa(ps_euk, (division %in% c("Chlorophyta", "Cryptophyta", 
    "Rhodophyta", "Haptophyta", "Ochrophyta")) | ((division == "Dinoflagellata") & 
    (class != "Syndiniales")) | (class == "Filosa-Chlorarachnea"))
cat("\nPhyloseq Photosynthetic Eukaryotes \n========== \n")

Phyloseq Photosynthetic Eukaryotes 
========== 
ps_photo
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 267 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 267 taxa by 8 taxonomic ranks ]
ps_hetero <- subset_taxa(ps_euk, !(division %in% c("Chlorophyta", "Cryptophyta", 
    "Rhodophyta", "Haptophyta", "Ochrophyta")) & !((division == "Dinoflagellata") & 
    !(class == "Syndiniales")) & !(class == "Filosa-Chlorarachnea"))
cat("\nPhyloseq Heterotrophic Eukaryotes \n========== \n")

Phyloseq Heterotrophic Eukaryotes 
========== 
ps_hetero
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 396 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 396 taxa by 8 taxonomic ranks ]

6.3 Create phyloseq files for proks

otu_table_prok <- otu_table %>% filter(taxo1 %in% c("Bacteria", "Archaea"))

otu_mat <- otu_table_prok %>% select(otu = otu_id, matches("EC|PR|RM|SBW|STJ"))

tax_mat <- otu_table_prok %>% select(otu = otu_id, taxo1:taxo7) %>% rename(kingdom = taxo1, 
    supergroup = taxo2, division = taxo3, class = taxo4, order = taxo5, family = taxo6, 
    genus = taxo7) %>% mutate(species = NA)

samples_df <- sample_table %>% rename(sample = sample_id)

row.names(otu_mat) <- otu_mat$otu
otu_mat <- otu_mat %>% select(-otu)
row.names(tax_mat) <- tax_mat$otu
tax_mat <- tax_mat %>% select(-otu)
row.names(samples_df) <- samples_df$sample
samples_df <- samples_df %>% select(-sample)
otu_mat <- as.matrix(otu_mat)
tax_mat <- as.matrix(tax_mat)

OTU = otu_table(otu_mat, taxa_are_rows = TRUE)
TAX = tax_table(tax_mat)
samples = sample_data(samples_df)

ps_prok <- phyloseq(OTU, TAX, samples)
ps_prok <- subset_samples(ps_prok, sequence_quality == "good")

cat("\nPhyloseq Prokaryotes \n========== \n")

Phyloseq Prokaryotes 
========== 
ps_hetero
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 396 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 396 taxa by 8 taxonomic ranks ]

6.4 Normalize number of reads in each sample using median sequencing depth.

  • ! If there no cells do not transform, just set column to 0 function(x, t=total_hetero) (if(sum(x) > 0){ t * (x / sum(x))} else {x})
# First define a function to normalize

ps_normalize_median <- function(ps, title) {
    ps_median = median(sample_sums(ps))
    cat(sprintf("\nThe median number of reads used for normalization of %s is  %.0f", 
        title, ps_median))
    normalize_median = function(x, t = ps_median) (if (sum(x) > 0) {
        t * (x/sum(x))
    } else {
        x
    })
    ps = transform_sample_counts(ps, normalize_median)
    cat(str_c("\nPhyloseq ", title, "\n========== \n"))
    print(ps)
}

# Apply to all the phyloseq files
ps_euk = ps_normalize_median(ps_euk, "eukaryotes (auto+hetero)")

The median number of reads used for normalization of eukaryotes (auto+hetero) is  4735
Phyloseq eukaryotes (auto+hetero)
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 663 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 663 taxa by 8 taxonomic ranks ]
ps_photo = ps_normalize_median(ps_photo, "eukaryotes autotrophs")

The median number of reads used for normalization of eukaryotes autotrophs is  3063
Phyloseq eukaryotes autotrophs
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 267 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 267 taxa by 8 taxonomic ranks ]
ps_hetero = ps_normalize_median(ps_hetero, "eukaryotes heterotrophs")

The median number of reads used for normalization of eukaryotes heterotrophs is  983
Phyloseq eukaryotes heterotrophs
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 396 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 396 taxa by 8 taxonomic ranks ]
ps_prok = ps_normalize_median(ps_prok, "prokaryotes")

The median number of reads used for normalization of prokaryotes is  54273
Phyloseq prokaryotes
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 2079 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 2079 taxa by 8 taxonomic ranks ]

6.5 Phyloseq files for abundant taxa

  • Remove taxa that are < 0.10 (euks) and <0.05 (proks) in any given sample
  • Normalize again…
ps_abundant <- function(ps, contrib_min = 0.1, title) {
    total_per_sample <- max(sample_sums(ps))
    ps <- filter_taxa(ps, function(x) sum(x > total_per_sample * contrib_min) > 
        0, TRUE)
    ps <- ps_normalize_median(ps, title)
}

cat("Remove taxa in low abundance \n\n")
Remove taxa in low abundance 
ps_euk_abundant = ps_abundant(ps_euk, contrib_min = 0.1, "eukaryotes (auto+hetero)")

The median number of reads used for normalization of eukaryotes (auto+hetero) is  3359
Phyloseq eukaryotes (auto+hetero)
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 60 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 60 taxa by 8 taxonomic ranks ]
ps_photo_abundant = ps_abundant(ps_photo, contrib_min = 0.1, "eukaryotes autotrophs")

The median number of reads used for normalization of eukaryotes autotrophs is  2801
Phyloseq eukaryotes autotrophs
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 61 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 61 taxa by 8 taxonomic ranks ]
ps_hetero_abundant = ps_abundant(ps_hetero, contrib_min = 0.1, "eukaryotes heterotrophs")

The median number of reads used for normalization of eukaryotes heterotrophs is  687
Phyloseq eukaryotes heterotrophs
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 81 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 81 taxa by 8 taxonomic ranks ]
ps_prok_abundant = ps_abundant(ps_prok, contrib_min = 0.05, "prokaryotes")

The median number of reads used for normalization of prokaryotes is  24353
Phyloseq prokaryotes
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 44 taxa and 81 samples ]
sample_data() Sample Data:       [ 81 samples by 22 sample variables ]
tax_table()   Taxonomy Table:    [ 44 taxa by 8 taxonomic ranks ]

6.6 Create a list for the auto and hetero phyloseq files

ps_list <- list(ps = c(ps_prok, ps_euk, ps_photo), title = c("Prokaryotes - all OTUs", 
    "Eukaryotes - Auto and Hetero - all OTUs", "Eukaryotes - Autotrophs - all OTUs"))
ps_list_abundant <- list(ps = c(ps_prok_abundant, ps_euk_abundant, ps_photo_abundant), 
    title = c("Prokaryotes - abundant OTUs (> 5%)", "Eukaryotes - Auto + Hetero - abundant OTUs (> 10%)", 
        "Eukaryotes - Autotrophs - abundant OTUs (> 10%)"))

6.7 Create tabular files for other plots (only for eukaryotes)

ps_to_long <- function(ps) {
    otu_df <- data.frame(otu_table(ps)) %>% rownames_to_column(var = "otu_id")
    taxo_df <- data.frame(tax_table(ps)) %>% rownames_to_column(var = "otu_id")
    otu_df <- left_join(taxo_df, otu_df)
    otu_df <- gather(otu_df, "sample", "n_seq", contains("X"))  # All samples contain X
}

long_euk <- ps_to_long(ps_euk)
long_photo <- ps_to_long(ps_photo)
long_euk_abundant <- ps_to_long(ps_euk_abundant)
long_photo_abundant <- ps_to_long(ps_photo_abundant)

6.8 Treemaps at division and class levels

treemap_dv(long_euk, c("division", "class"), "n_seq", "All euks")

treemap_dv(long_photo, c("division", "class"), "n_seq", "Photo euks")

6.9 Most abundant species

long_euk_species <- long_euk %>% mutate(species_label = str_c(class, species, 
    sep = "-")) %>% group_by(class, species, species_label) %>% summarize(n_seq = sum(n_seq)) %>% 
    arrange(desc(n_seq)) %>% ungroup()

ggplot(top_n(long_euk_species, 30, n_seq)) + geom_col(aes(x = reorder(species, 
    n_seq), y = n_seq, fill = class)) + coord_flip() + xlab("Species") + ylab("Number of species")

6.10 Bar plot of divisions per station

Note: some stations are completely missing heterotrophs (Only Opistokonta)

for (i in 1:3) {
    p <- plot_bar(ps_list$ps[[i]], x = "sample_label", fill = "division") + 
        geom_bar(aes(color = division, fill = division), stat = "identity", 
            position = "stack") + ggtitle(str_c("Division level - ", ps_list$title[[i]])) + 
        theme(axis.text.y = element_text(size = 10)) + theme(axis.text.x = element_text(angle = 0, 
        hjust = 0.5)) + coord_flip()
    print(p)
}

6.11 Bar plot of class per station

Only consider the abundant taxa

for (i in 1:3) {
    p <- plot_bar(ps_list_abundant$ps[[i]], x = "sample_label", fill = "class") + 
        geom_bar(aes(color = class, fill = class), stat = "identity", position = "stack") + 
        ggtitle(str_c("Class level - ", ps_list_abundant$title[[i]])) + theme(axis.text.y = element_text(size = 10)) + 
        theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) + coord_flip()
    print(p)
}

6.12 Compare by Straight, Site, Moonsoon (abundant OTUs only)

for (factor in c("strait", "location", "monsoon")) {
    for (i in 1:3) {
        ps_aggregate <- merge_samples(ps_list_abundant$ps[[i]], factor)
        ps_aggregate <- transform_sample_counts(ps_aggregate, function(x) 100 * 
            (x/sum(x)))
        p <- plot_bar(ps_aggregate, fill = "division") + geom_col(aes(color = division, 
            fill = division)) + ggtitle(str_c(ps_list_abundant$title[[i]], " - ", 
            factor)) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, 
            hjust = 1)) + ylab("%")
        print(p)
    }
}

6.13 Main taxa

6.13.1 Main genera for different division of Eukaryotes (Autotrophs)

for (one_division in c("Chlorophyta", "Dinoflagellata", "Ochrophyta")) {
    ps_subset <- subset_taxa(ps_photo_abundant, division %in% one_division)
    p <- plot_bar(ps_subset, x = "genus", facet_grid = ~strait) + geom_col() + 
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) + 
        ggtitle(str_c(one_division, " - Abundant OTUs")) + coord_flip()
    print(p)
}

6.13.2 Main species of Mamiellophyceae and Diatoms (Eukaryotes - Autrotrophs)

for (one_class in c("Mamiellophyceae", "Dinophyceae", "Bacillariophyta")) {
    ps_subset <- subset_taxa(ps_photo_abundant, class %in% one_class)
    p <- plot_bar(ps_subset, x = "species", facet_grid = ~strait) + geom_col() + 
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) + 
        ggtitle(str_c(one_class, " - Abundant OTUs")) + coord_flip()
    print(p)
}

6.14 Heatmaps (abundant OTUs)

  • Data are agglomated at the genus level. Use function tax_glom
for (i in c(1)) {
    ps_heat <- tax_glom(ps_list_abundant$ps[[i]], taxrank = "family")
    p <- plot_heatmap(ps_heat, method = "NMDS", distance = "bray", taxa.label = "family", 
        taxa.order = "division", sample.label = "sample_label", sample.order = "sample_label", 
        low = "beige", high = "red", na.value = "beige", title = ps_list_abundant$title[[i]])
    print(p)
}

for (i in 2:3) {
    
    ps_heat <- tax_glom(ps_list_abundant$ps[[i]], taxrank = "genus")
    p <- plot_heatmap(ps_heat, method = "NMDS", distance = "bray", taxa.label = "genus", 
        taxa.order = "division", sample.label = "sample_label", sample.order = "sample_label", 
        low = "beige", high = "red", na.value = "beige", title = ps_list_abundant$title[[i]])
    print(p)
}

6.15 NMDS

Sample removed because they were pulling the NMDS * PR2X16XS21 it has a single eukaryote (diatom bloom ) * RM13XS36 cause problem for bacteria * PR11XS25 cause problem for hetero euks * SBW11XS26 cause problem for hetero euks * SBW13XS37 cause problem for hetero euks * RM13XS36 cause problem for hetero euks

Define function

ps_nmds <- function(ps_list) {
    for (i in 1:3) {
        ps_nmds <- ps_list$ps[[i]]
        
        # Remove samples with no reads
        ps_nmds <- prune_samples(sample_sums(ps_nmds) > 0, ps_nmds)
        
        # Remove samples that caused problems (1= prok, 2=euk, 3=euk auto)
        if (i == 1) 
            {
                ps_nmds <- prune_samples(!(sample_names(ps_nmds) %in% c("RM13XS36")), 
                  ps_nmds)
            }  # Prokaryotes
        if (i %in% c(2, 3)) 
            {
                ps_nmds <- prune_samples(!(sample_names(ps_nmds) %in% c("PR2X16SXS21")), 
                  ps_nmds)
            }  # Eukaryotes
        if (i == 4) 
            {
                ps_nmds <- prune_samples(!(sample_names(ps_nmds) %in% c("PR11XS25", 
                  "SBW11XS26", "SBW13XS37")), ps_nmds)
            }  # Heterotrophs not used
        
        singa.ord <- ordinate(ps_nmds, "NMDS", "bray")
        
        # Factor to move the labels
        nudge <- singa.ord[["points"]] * 0.05
        
        p <- plot_ordination(ps_nmds, singa.ord, type = "samples", color = "strait", 
            shape = "monsoon", title = ps_list$title[[i]]) + geom_point(size = 5) + 
            scale_color_manual(values = c("red", "green", "blue")) + scale_shape_manual(values = c(17, 
            18, 15, 16)) + geom_text(aes(label = location_label), nudge_x = nudge, 
            nudge_y = nudge, check_overlap = FALSE, size = 3)
        theme_bw()
        print(singa.ord)
        print(p)
        
        p <- plot_ordination(ps_nmds, singa.ord, type = "taxa", color = "supergroup", 
            shape = "supergroup", title = ps_list$title[[i]]) + geom_point(size = 3) + 
            theme_bw()
        print(p)
    }
}

6.15.1 All OTUs

ps_nmds(ps_list)
Square root transformation
Wisconsin double standardization
Run 0 stress 0.12 
Run 1 stress 0.14 
Run 2 stress 0.13 
Run 3 stress 0.14 
Run 4 stress 0.14 
Run 5 stress 0.13 
Run 6 stress 0.13 
Run 7 stress 0.12 
... New best solution
... Procrustes: rmse 0.047  max resid 0.14 
Run 8 stress 0.14 
Run 9 stress 0.13 
Run 10 stress 0.14 
Run 11 stress 0.13 
Run 12 stress 0.12 
Run 13 stress 0.13 
Run 14 stress 0.13 
Run 15 stress 0.14 
Run 16 stress 0.14 
Run 17 stress 0.14 
Run 18 stress 0.13 
Run 19 stress 0.14 
Run 20 stress 0.14 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(sqrt(veganifyOTU(physeq))) 
Distance: bray 

Dimensions: 2 
Stress:     0.12 
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))' 

Square root transformation
Wisconsin double standardization
Run 0 stress 0.19 
Run 1 stress 0.18 
... New best solution
... Procrustes: rmse 0.059  max resid 0.28 
Run 2 stress 0.2 
Run 3 stress 0.19 
Run 4 stress 0.19 
Run 5 stress 0.19 
Run 6 stress 0.19 
Run 7 stress 0.2 
Run 8 stress 0.19 
Run 9 stress 0.19 
Run 10 stress 0.19 
Run 11 stress 0.18 
Run 12 stress 0.19 
Run 13 stress 0.18 
Run 14 stress 0.19 
Run 15 stress 0.18 
Run 16 stress 0.19 
Run 17 stress 0.18 
Run 18 stress 0.19 
Run 19 stress 0.19 
Run 20 stress 0.19 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(sqrt(veganifyOTU(physeq))) 
Distance: bray 

Dimensions: 2 
Stress:     0.18 
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))' 

Square root transformation
Wisconsin double standardization
Run 0 stress 0.18 
Run 1 stress 0.17 
... New best solution
... Procrustes: rmse 0.063  max resid 0.25 
Run 2 stress 0.18 
Run 3 stress 0.18 
Run 4 stress 0.17 
... New best solution
... Procrustes: rmse 0.048  max resid 0.23 
Run 5 stress 0.18 
Run 6 stress 0.18 
Run 7 stress 0.17 
Run 8 stress 0.18 
Run 9 stress 0.18 
Run 10 stress 0.17 
Run 11 stress 0.18 
Run 12 stress 0.18 
Run 13 stress 0.17 
Run 14 stress 0.17 
Run 15 stress 0.18 
Run 16 stress 0.19 
Run 17 stress 0.19 
Run 18 stress 0.19 
Run 19 stress 0.18 
Run 20 stress 0.18 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(sqrt(veganifyOTU(physeq))) 
Distance: bray 

Dimensions: 2 
Stress:     0.17 
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))'

6.15.2 Abundant OTUs

ps_nmds(ps_list_abundant)
Square root transformation
Wisconsin double standardization
Run 0 stress 0.12 
Run 1 stress 0.15 
Run 2 stress 0.13 
Run 3 stress 0.41 
Run 4 stress 0.12 
... Procrustes: rmse 4.7e-05  max resid 0.00034 
... Similar to previous best
Run 5 stress 0.14 
Run 6 stress 0.13 
Run 7 stress 0.12 
Run 8 stress 0.14 
Run 9 stress 0.14 
Run 10 stress 0.13 
Run 11 stress 0.12 
Run 12 stress 0.13 
Run 13 stress 0.12 
Run 14 stress 0.12 
Run 15 stress 0.12 
Run 16 stress 0.15 
Run 17 stress 0.15 
Run 18 stress 0.12 
Run 19 stress 0.13 
Run 20 stress 0.12 
*** Solution reached

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(sqrt(veganifyOTU(physeq))) 
Distance: bray 

Dimensions: 2 
Stress:     0.12 
Stress type 1, weak ties
Two convergent solutions found after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))' 

Square root transformation
Wisconsin double standardization
Run 0 stress 0.17 
Run 1 stress 0.17 
Run 2 stress 0.17 
Run 3 stress 0.17 
Run 4 stress 0.17 
Run 5 stress 0.17 
Run 6 stress 0.18 
Run 7 stress 0.18 
Run 8 stress 0.18 
Run 9 stress 0.17 
... New best solution
... Procrustes: rmse 0.049  max resid 0.24 
Run 10 stress 0.17 
Run 11 stress 0.17 
Run 12 stress 0.18 
Run 13 stress 0.17 
Run 14 stress 0.17 
Run 15 stress 0.17 
Run 16 stress 0.17 
Run 17 stress 0.18 
Run 18 stress 0.17 
Run 19 stress 0.17 
Run 20 stress 0.17 
... Procrustes: rmse 0.052  max resid 0.23 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(sqrt(veganifyOTU(physeq))) 
Distance: bray 

Dimensions: 2 
Stress:     0.17 
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))' 

Square root transformation
Wisconsin double standardization
Run 0 stress 0.16 
Run 1 stress 0.16 
Run 2 stress 0.17 
Run 3 stress 0.16 
Run 4 stress 0.16 
Run 5 stress 0.17 
Run 6 stress 0.17 
Run 7 stress 0.17 
Run 8 stress 0.16 
Run 9 stress 0.17 
Run 10 stress 0.17 
Run 11 stress 0.17 
Run 12 stress 0.17 
Run 13 stress 0.16 
Run 14 stress 0.17 
Run 15 stress 0.17 
Run 16 stress 0.17 
Run 17 stress 0.17 
Run 18 stress 0.16 
Run 19 stress 0.17 
Run 20 stress 0.16 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(sqrt(veganifyOTU(physeq))) 
Distance: bray 

Dimensions: 2 
Stress:     0.16 
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))'

6.16 Network analysis

for (i in 1:3) {
    
    ps_nmds <- ps_list_abundant$ps[[i]]
    
    # Remove samples with no reads
    ps_nmds <- prune_samples(sample_sums(ps_nmds) > 0, ps_nmds)
    
    # Remove samples that caused problems (1= prok, 2=euk, 3=euk auto)
    if (i == 1) 
        {
            ps_nmds <- prune_samples(!(sample_names(ps_nmds) %in% c("RM13XS36")), 
                ps_nmds)
        }  # Prokaryotes
    if (i %in% c(2, 3)) 
        {
            ps_nmds <- prune_samples(!(sample_names(ps_nmds) %in% c("PR2X16SXS21")), 
                ps_nmds)
        }  # Eukaryotes
    if (i == 4) 
        {
            ps_nmds <- prune_samples(!(sample_names(ps_nmds) %in% c("PR11XS25", 
                "SBW11XS26", "SBW13XS37")), ps_nmds)
        }  # Heterotrophs not used
    
    if (i > 1) {
        p <- plot_net(ps_nmds, distance = "(A+B-2*J)/(A+B)", type = "taxa", 
            maxdist = 0.4, color = "class", point_label = "genus") + ggtitle(ps_list_abundant$title[[i]])
    } else {
        p <- plot_net(ps_nmds, distance = "(A+B-2*J)/(A+B)", type = "taxa", 
            maxdist = 0.4, color = "class", point_label = "family") + ggtitle(ps_list_abundant$title[[i]])
    }
    
    print(p)
}

Daniel Vaulot, Adriana Lopes dos Santos

23 09 2018